ReAct agents, which interleave reasoning and action, are a popular pattern for building LLM-powered tools. However, a common failure mode is the agent entering an infinite loop, repeatedly calling the same tool or rephrasing the same query without progress. The root causes typically include ambiguous tool outputs that don't change the agent's state, overly permissive loop termination conditions, or a lack of memory to track previous attempts. Developers can mitigate this by implementing explicit step limits, adding state-change detection, and designing tools that return structured feedback. This issue is especially relevant as agent-based applications move from prototypes to production, where reliability is critical. Understanding these failure patterns is essential for building robust autonomous systems.
ReAct agents often fall into ineffective loops due to ambiguous tool feedback or poorly designed stopping criteria. This signal highlights a recurring issue in agent development and offers a starting point for more robust loop control.